{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:10:31Z","timestamp":1790053831499,"version":"4.0.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s00371-026-04419-9","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T11:54:43Z","timestamp":1773748483000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-domain fusion network for enhanced polyp segmentation in colonoscopy images"],"prefix":"10.1007","volume":"42","author":[{"given":"Xiaolong","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaofan","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changjiang","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"issue":"7","key":"4419_CR1","doi-asserted-by":"publisher","first-page":"2995","DOI":"10.1109\/JBHI.2022.3147686","volume":"26","author":"DC Wang","year":"2022","unstructured":"Wang, D.C., Chen, S.J., Sun, X.Z., et al.: AFP-mask: anchor-free polyp instance segmentation in colonoscopy. IEEE J. Biomed. Health Inform. 26(7), 2995\u20133006 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"3","key":"4419_CR2","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., Ferlay, J., Siegel, R.L., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209\u2013249 (2021)","journal-title":"CA Cancer J. Clin."},{"issue":"1","key":"4419_CR3","first-page":"451","volume":"10","author":"BS Chhikara","year":"2023","unstructured":"Chhikara, B.S., Parang, K.: Global cancer statistics 2022: the trends projection analysis. Chem. Biol. Lett. 10(1), 451 (2023)","journal-title":"Chem. Biol. Lett."},{"key":"4419_CR4","doi-asserted-by":"crossref","unstructured":"Li, Y., Hu, M., & Yang, X. (2023). Polyp-SAM: Transfer SAM for polyp segmentation. arXiv preprint arXiv:2305.00293.","DOI":"10.1117\/12.3006809"},{"key":"4419_CR5","first-page":"234","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp. 234\u2013241. Springer, Cham (2015)"},{"issue":"8","key":"4419_CR6","doi-asserted-by":"publisher","first-page":"4090","DOI":"10.1109\/JBHI.2022.3173948","volume":"26","author":"G Yue","year":"2022","unstructured":"Yue, G., Han, W., Jiang, B., Zhou, T., Cong, R., Wang, T.: Boundary constraint network with cross-layer feature integration for polyp segmentation. IEEE J. Biomed. Health Inform. 26(8), 4090\u20134099 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"4419_CR7","doi-asserted-by":"publisher","first-page":"10205","DOI":"10.1007\/s00371-025-04029-x","volume":"41","author":"G Li","year":"2025","unstructured":"Li, G., Li, S., Lin, Y., et al.: Cfseg-Net: context feature extraction network for medical image segmentation. Vis. Comput. 41, 10205\u201310215 (2025). https:\/\/doi.org\/10.1007\/s00371-025-04029-x","journal-title":"Vis. Comput."},{"key":"4419_CR8","unstructured":"Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752."},{"issue":"1","key":"4419_CR9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/JBHI.2016.2637004","volume":"21","author":"LQ Yu","year":"2016","unstructured":"Yu, L.Q., Chen, H., Dou, Q., et al.: Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J. Biomed. Health Inform. 21(1), 65\u201375 (2016)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"4419_CR10","doi-asserted-by":"crossref","unstructured":"Tomar, N. K., Srivastava, A., Bagci, U., & Jha, D. (2022). Automatic polyp segmentation with multiple kernel dilated convolution network. In: Proceedings of the IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 317\u2013322). Shenzhen, China: IEEE.","DOI":"10.1109\/CBMS55023.2022.00063"},{"key":"4419_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3506630","volume":"63","author":"Y Xie","year":"2025","unstructured":"Xie, Y., et al.: Localization, balance, and affinity: a stronger multifaceted collaborative salient object detector in remote sensing images. IEEE Trans. Geosci. Remote Sens. 63, 1\u201317 (2025). https:\/\/doi.org\/10.1109\/TGRS.2024.3506630","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4419_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.103950","author":"Y Xie","year":"2024","unstructured":"Xie, Y., Zhan, N., Zhu, J., Xu, B., Chen, H., Mao, W., Luo, X., Hu, Y.: Landslide extraction from aerial imagery considering context association characteristics. Int. J. Appl. Earth Obs. Geoinf. (2024). https:\/\/doi.org\/10.1016\/j.jag.2024.103950","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"1","key":"4419_CR13","doi-asserted-by":"publisher","first-page":"2346259","DOI":"10.1080\/17538947.2024.2346259","volume":"17","author":"J Zhu","year":"2024","unstructured":"Zhu, J., Zhang, J., Chen, H., Xie, Y., Gu, H., Lian, H.: A cross-view intelligent person search method based on multi feature constraints. Int. J. Digit. Earth. 17(1), 2346259 (2024)","journal-title":"Int. J. Digit. Earth."},{"key":"4419_CR14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4419_CR15","doi-asserted-by":"crossref","unstructured":"Lei, M., Wu, H., Lv, X., & Wang, X. (2025). ConDSeg: A general medical image segmentation framework via contrast-driven feature enhancement. In: Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 5, pp. 4571\u20134579).","DOI":"10.1609\/aaai.v39i5.32482"},{"key":"4419_CR16","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L. (2020). Pranet: Parallel reverse attention network for polyp segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham pp 263\u2013273","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"4419_CR17","doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, Q., Tang, F., Meng, J., Su, J., & Song, S. (2022). Stepwise feature fusion: Local guides global. In L. Wang et al. (Eds.), Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022 (Lecture Notes in Computer Science, Vol. 13433). Cham, Switzerland: Springer.","DOI":"10.1007\/978-3-031-16437-8_11"},{"key":"4419_CR18","doi-asserted-by":"crossref","unstructured":"Tomar, N. K., Jha, D., Bagci, U., & Ali, S. TGANet: Text-guided attention for improved polyp segmentation. In: Wang L., et al. (eds.), Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022 (Lecture Notes in Computer Science, Vol. 13433). Springer, Cham (2022)","DOI":"10.1007\/978-3-031-16437-8_15"},{"key":"4419_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al. Lesion-aware dynamic kernel for polyp segmentation. In L. Wang et al. (Eds.), Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022 (Lecture Notes in Computer Science, Vol. 13433). Springer, Cham (2022)","DOI":"10.1007\/978-3-031-16437-8_10"},{"key":"4419_CR20","doi-asserted-by":"crossref","unstructured":"Cai, L., et al. Using guided self-attention with local information for polyp segmentation. In Wang L.,et al. (eds.), Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022 (Lecture Notes in Computer Science, Vol. 13434). Springer, Cham (2022)","DOI":"10.1007\/978-3-031-16440-8_60"},{"key":"4419_CR21","doi-asserted-by":"crossref","unstructured":"Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S. K., & Cui, S. (2021). Shallow attention network for polyp segmentation. In: de Bruijne M., et al. (eds.), Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021 (Lecture Notes in Computer Science, Vol. 12901)., Springer, Cham (2021)","DOI":"10.1007\/978-3-030-87193-2_66"},{"key":"4419_CR22","doi-asserted-by":"crossref","unstructured":"Shen, Y., Jia, X., & Meng, M. Q.-H. HRENet: A hard region enhancement network for polyp segmentation. In: de Bruijne M., et al. (Eds.), Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021 (Lecture Notes in Computer Science, Vol. 12901). Springer, Cham (2021)","DOI":"10.1007\/978-3-030-87193-2_53"},{"key":"4419_CR23","doi-asserted-by":"crossref","unstructured":"Tang, F., et al. DuAT: Dual-aggregation transformer network for medical image segmentation. In: Liu Q., et al. (eds.), Pattern Recognition and Computer Vision (PRCV 2023) (Lecture Notes in Computer Science, Vol. 14429). Springer, Singapore (2024)","DOI":"10.1007\/978-981-99-8469-5_27"},{"key":"4419_CR24","doi-asserted-by":"publisher","DOI":"10.26599\/AIR.2023.9150015","volume":"2","author":"B Dong","year":"2023","unstructured":"Dong, B., Wang, W., Fan, D.-P., et al.: Polyp-PVT: polyp segmentation with pyramid vision transformers. CAAI Artif. Intell. Res. 2, 9150015 (2023)","journal-title":"CAAI Artif. Intell. Res."},{"key":"4419_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/access.2025.3544187","author":"J Ren","year":"2025","unstructured":"Ren, J., Zhang, X., Zhang, L.: HiFiSeg: High-frequency information enhanced polyp segmentation with global-local vision transformer. IEEE Access (2025). https:\/\/doi.org\/10.1109\/access.2025.3544187","journal-title":"IEEE Access"},{"key":"4419_CR26","doi-asserted-by":"crossref","unstructured":"Li, H., Zhang, D., Yao, J., Han, L., Li, Z., & Han, J. ASPS: Augmented Segment Anything Model for polyp segmentation. In: Linguraru, M. G., et al. (eds.), Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024 (Lecture Notes in Computer Science, Vol. 15009). Springer, Cham (2024)","DOI":"10.1007\/978-3-031-72114-4_12"},{"key":"4419_CR27","doi-asserted-by":"publisher","first-page":"10395","DOI":"10.1007\/s00371-025-04044-y","volume":"41","author":"N Ahmed","year":"2025","unstructured":"Ahmed, N., Tan, X., Ma, L.: D2U-Net: a dual-path hybrid U-Net architecture for precise medical image segmentation. Vis. Comput. 41, 10395\u201310415 (2025)","journal-title":"Vis. Comput."},{"key":"4419_CR28","doi-asserted-by":"crossref","unstructured":"Li, Y., Lu, J., Chen, H., Wu, X., & Chen, X. (2023). Dilated convolutional transformer for high-quality image deraining. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops.","DOI":"10.1109\/CVPRW59228.2023.00442"},{"key":"4419_CR29","unstructured":"Dauphin, Y. N., Fan, A., Auli, M., & Grangier, D. (2017). Language modeling with gated convolutional networks. In: Proceedings of the International Conference on Machine Learning"},{"key":"4419_CR30","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal, J., S\u00e1nchez, F.J., Fern\u00e1ndez-Esparrach, G., Gil, D., Rodr\u00edguez, C., Vilari\u00f1o, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99\u2013111 (2015)","journal-title":"Comput. Med. Imaging Graph."},{"key":"4419_CR31","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9, 283\u2013293 (2014)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"2","key":"4419_CR32","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","volume":"35","author":"N Tajbakhsh","year":"2015","unstructured":"Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35(2), 630\u2013644 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4419_CR33","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P. H., Riegler, M. A., Halvorsen, P., de Lange, T., Johansen, D., & Johansen, H. D. (2020). Kvasir-seg: A segmented polyp dataset. In: Proceedings of the 26th International Conference on MultiMedia Modeling (MMM 2020) (pp. 451\u2013462). Cham, Switzerland: Springer.","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"4419_CR34","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/4037190","volume":"2017","author":"D V\u00e1zquez","year":"2017","unstructured":"V\u00e1zquez, D., Bernal, J., S\u00e1nchez, F.J., Fern\u00e1ndez-Esparrach, G., L\u00f3pez, A.M., Romero, A., Drozdzal, M., Courville, A.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthc. Eng. 2017, 4037190 (2017)","journal-title":"J. Healthc. Eng."},{"key":"4419_CR35","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., & S\u00fcsstrunk, S. (2009). Frequency-tuned salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1597\u20131604). IEEE.","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"4419_CR36","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Cheng, M., Liu, Y., Li, T., & Borji, A. (2017). A new way to evaluate foreground maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/ICCV.2017.487"},{"key":"4419_CR37","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., & Borji, A. (2018). Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI) (pp. 698\u2013704).","DOI":"10.24963\/ijcai.2018\/97"},{"issue":"6","key":"4419_CR38","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4419_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109555","volume":"140","author":"T Zhou","year":"2023","unstructured":"Zhou, T., Zhou, Y., He, K., Gong, C., Yang, J., Fu, H., Shen, D.: Cross-level feature aggregation network for polyp segmentation. Pattern Recognit. 140, 109555 (2023)","journal-title":"Pattern Recognit."},{"key":"4419_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106704","volume":"155","author":"K Wang","year":"2023","unstructured":"Wang, K., Liu, L., Fu, X., Liu, L., Peng, W.: Ra-denet: reverse attention and distractions elimination network for polyp segmentation. Comput. Biol. Med. 155, 106704 (2023)","journal-title":"Comput. Biol. Med."},{"key":"4419_CR41","doi-asserted-by":"crossref","unstructured":"Bui, N.-T., Hoang, D.-H., Nguyen, Q.-T., Tran, M.-T., & Le, N. (2024). Meganet: Multi-scale edge-guided attention network for weak boundary polyp segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (pp. 7985\u20137994).","DOI":"10.1109\/WACV57701.2024.00780"},{"key":"4419_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, X., Jia, H., Pang, Y., Lv, L., Tian, F., Zhang, L., Sun, W., & Lu, H. (2023). M2SNet: Multi-scale in multi-scale subtraction network for medical image segmentation. arXiv preprint arXiv:2303.10894.","DOI":"10.1016\/j.bspc.2023.105330"},{"key":"4419_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110554","volume":"154","author":"Z Yu","year":"2024","unstructured":"Yu, Z., Zhao, L., Liao, T., Zhang, X., Chen, G., Xiao, G.: A novel non-pretrained deep supervision network for polyp segmentation. Pattern Recognit. 154, 110554 (2024)","journal-title":"Pattern Recognit."},{"key":"4419_CR44","unstructured":"Child, R., Gray, S., Radford, A., & Sutskever, I. (2019). Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509"},{"key":"4419_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, K., & Liu, D. (2023). Customized segment anything model for medical image segmentation. arXiv preprint arXiv:2304.13785","DOI":"10.2139\/ssrn.4495221"},{"issue":"8","key":"4419_CR46","doi-asserted-by":"publisher","first-page":"3196","DOI":"10.1109\/TMI.2025.3559402","volume":"44","author":"Y Chu","year":"2025","unstructured":"Chu, Y., et al.: Improving representation of high-frequency components for medical visual foundation models. IEEE Trans. Med. Imaging 44(8), 3196\u20133209 (2025). https:\/\/doi.org\/10.1109\/TMI.2025.3559402","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04419-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04419-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04419-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T13:39:53Z","timestamp":1775741993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04419-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":46,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["4419"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04419-9","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"26 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"209"}}